The problem of consensus-based distributed state estimation of a non-linear dynamical system in the presence of multiplicative observation noise is investigated in this study. Generalised extended information filter (...
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The problem of consensus-based distributed state estimation of a non-linear dynamical system in the presence of multiplicative observation noise is investigated in this study. Generalised extended information filter (GEIF) is developed for non-linear state estimation in the information-space framework. To fuse the information contribution of local estimators, an average consensus algorithm is employed. To achieve faster convergence towards consensus, a novel technique is proposed to modify the consensus weights, adaptively. Computational complexity of the proposed estimator is also analysed theoretically to demonstrate the computational advantage of the adaptive consensus-based distributed GEIF over the centralised counterpart. Moreover, stability of local estimators in terms of mean-square boundedness of state estimation error is guaranteed, in the presence of multiplicative noise. Simulation results are provided to evaluate performance of the proposed adaptive distributed estimator for a target-tracking problem in a wireless sensor network.
Distributed algorithms for an aggregate function estimation are an important complement of many real-life applications based on wireless sensor networks. Achieving a high precision of an estimation in a shorter time c...
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Distributed algorithms for an aggregate function estimation are an important complement of many real-life applications based on wireless sensor networks. Achieving a high precision of an estimation in a shorter time can optimize the overall energy consumption. Therefore, the choice of a proper distributed algorithm is an important part of an application design. In this study, we focus our attention on the average consensus algorithm and evaluate six weight models appropriate for the implementation into real-life applications. Our aim is to find the most suitable model in terms of the estimation precision in various phases of the algorithm. We examine the deviation of the least precise estimate over iterations for a Gaussian, a Uniform and a Bernoulli distribution of the initial states in strongly and weakly connected networks with a randomly generated topology. We examine which model is the most and the least precise in various phases. Based on these findings, we determine the most suitable model for real-life applications.
This study tackles common challenges of the average consensus algorithms in real-world distributed wireless networks. The drawbacks of asynchronous transmissions, topology changes and communication delays on the distr...
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This study tackles common challenges of the average consensus algorithms in real-world distributed wireless networks. The drawbacks of asynchronous transmissions, topology changes and communication delays on the distributed decision making of the network members to terminate a consensusalgorithm are mainly studied. Two distributed stopping policies are proposed that help the network members to gradually stop message transmissions when they reach an acceptable accuracy of the final consensus value. Simulation results show that the proposed algorithms help previous consensusalgorithms to decrease total packet transmissions in a random topology network. Simulation results also confirm that the proposed can perform in the presence of delayed members and non-ideal transmissions.
An energy-efficient estimation of an aggregate function can significantly optimize a global event detection or monitoring in wireless sensor networks. This is probably the main reason why an optimization of the comple...
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An energy-efficient estimation of an aggregate function can significantly optimize a global event detection or monitoring in wireless sensor networks. This is probably the main reason why an optimization of the complementary consensusalgorithms is one of the key challenges of the lifetime extension of the wireless sensor networks on which the attention of many scientists is paid. In this paper, we introduce an optimized weight model for the average consensus algorithm. It is called the Biphasically configured Metropolis-Hasting weight model and is based on a modification of the Metropolis-Hasting weight model by rephrasing the initial configuration into two parts. The first one is the default configuration of the Metropolis-Hasting weight model, while, the other one is based on a recalculation of the weights allocated to the adjacent nodes’ incoming values at the cost of decreasing the value of the weights of the inner states. The whole initial configuration is executed in a fully-distributed manner. In the experimental section, it is proven that our optimized weight model significantly optimizes the Metropolis-Hasting weight model in several aspects and achieves better results compared with other concurrent weight models.
The internet data center is growing swiftly in recent years as the Cloud computing is becoming popular. An internet data center is an apt example of a distributed network with dynamic loads, and a load balancing algor...
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ISBN:
(纸本)9781509009251
The internet data center is growing swiftly in recent years as the Cloud computing is becoming popular. An internet data center is an apt example of a distributed network with dynamic loads, and a load balancing algorithm is needed in a data center to manage and distribute the loading to all server nodes appropriately. The implementation of a load balancer brings benefits to both the data center owner and Cloud users as it prolongs the lifespan of servers and improve the quality of service. To design a load balancing algorithm, various existing algorithms are studied. An average consensus algorithm is proposed for implementation in a network of server nodes. For verification purposes, the proposed algorithm is tested with various network topologies for its adaptability to distributed communication variance to the demand for scalability. The performance results are obtained through a series of experimental simulations;the results are then presented and analyzed in comparison to the conventional algorithm. Keywords: Cloud Computing Technology, Graph Theory, Graph Topology, Load Balancing, average consensus algorithm
Based on the combination of global coherence field (GCF) and distributed particle filter (DPF) a speaker tracking method is proposed for distributed microphone networks in this paper. In the distributed microphone net...
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Based on the combination of global coherence field (GCF) and distributed particle filter (DPF) a speaker tracking method is proposed for distributed microphone networks in this paper. In the distributed microphone network, each node comprises a microphone pair, and its generalized cross-correlation (GCC) function is estimated. Based on the average over all local GCC observations, a global coherence field-based pseudo-likelihood (GCF-PL) function is developed as the likelihood for a DPF. In the proposed method, all nodes share an identical particle set, and each node performs local particle filtering simultaneously. In the local particle filter, the likelihood GCF-PL for each particle weight is computed with an average consensus algorithm. With an identical particle set and the consistent estimate of GCF-PL for each particle weight, all individual nodes possess a common particle presentation for the global posterior of the speaker state, which is utilized by each node for an estimated global speaker position. Employing the GCF-PL as the likelihood for DPF, no assumption is required about the independence of nodes observations as well as observation noise statistics. Additionally, only local information exchange occurs among neighboring nodes;and finally each node has a global estimate of the speaker position. Simulation results demonstrate the validity of the proposed method.
Pozoruhodný vývoj v oblasti počítačových systémov a ich vzájomnej komunikácie v ostatných viac ako 70 rokoch umožnil vznik alternatívy k dovtedy často využívaným ...
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Pozoruhodný vývoj v oblasti počítačových systémov a ich vzájomnej komunikácie v ostatných viac ako 70 rokoch umožnil vznik alternatívy k dovtedy často využívaným centralizovaným systémom. Moderné vysoko distribuované systémy sú tvorené nezávislými entitami plniacimi špecifické funkcionality ako jeden, pre užívateľa netransparentný, celok. Tento fakt mal za následok výrazné rozšírenie distribuovaných algoritmov a ich implementáciu v praxi. Časté uplatnenie našli hlavne distribuované algoritmy pre multisenzorovú dátovú fúziu zabezpečujúce výrazné zvýšenie kvality exekuovaných aplikácií. Táto dizertačná práca sa zaoberá optimalizáciou a analýzou distribuovaných systémov a to konkrétne distribuovanými algoritmami založenými na dosahnovaní konsenzu pre odhad agregačných funkcií (primárne pre odhad aritmetického priemeru distribuovaným spôsobom). Prvá kapitola je zameraná na teoretický základ týkajúci sa distribuovaných systémov, ich evolúciou, architektúrami, komparáciou a výhodami/nevýhodami v porovnaní so zmienenými centralizovanými systémami. Druhá kapitola je zameraná na multisenzorovú dátovú fúziu, jej praktické využitie, klasifikáciu distribuovaných algoritmov pre odhad agregačných funkcií, matematické modelovanie týchto algoritmov a prezentáciu frekventovane citovaných mechanizmov pre distribuované priemerovanie ako napríklad protokol Push-Sum, Metropolis-Hastings váhovací model, Best Constant váhovací model atď. Praktická časť pozostáva z prezentácie mechanizmov pre optimalizáciu distribuovaných systémov, návrhov nových algoritmov a komplementárnych mechanizmov pre distribuované systémy a ich analýzu ( resp. vzájomnú komparáciu) z hľadísk ako rýchlosť konvergencie, presnosť estimácie, robustnosť, implementovateľnosť do reálnych systémov atď.
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